IBM Turns Cell Phones Into Sensors To Monitor Public Transit

Everyone knows that when a company gathers personal information about you, via cookies on the web or settings on your smartphone, they’re likely to sell that information. Then, you end up on the receiving end of much unwanted contact, emails, phone calls, etc, from the company that bought the data. But what if this data sharing among companies could work to your advantage?

In an effort to improve urban development and public transportation, an IBM research team worked together with the telecom company Orange to tackle some of the service issues with bus routes in the Ivory Coast. Working around the nation’s largest city, Abidjan, Orange released 2.5 billion call records from five million cell phone users in the Ivory Coast. (The records were cleaned of personal identity before release).

IBM was then able to take this unprecedented mass of mobile data, and essentially turn cell phones into sensors and measuring sticks (conjuring images of the Joker-location system from The Dark Knight. Don’t worry – it’s not). IBM was able to use data such as aggregate communication between towers, mobility traces for location and movement, and categorical identifiers that indicate larger population trends.

While the data is rough due to phone capabilities and use-frequency in less-industrialized nations, IBM was still able to use this unique strategy to better inform urban development with more efficient bus routes. For example, of the possible improvements found, the team suggested that

adding two routes and extending an existing one would do the most to optimize the system, with a 10 percent time savings for commuters.

The project also holds greater implications for mobile-data-driven research. Francesco Calabrese, a researcher and coauthor of the report for IBM, thinks

This represents a new front with a potentially large impact on improving urban transportation systems . . . People with cell phones can serve as sensors and be the building blocks of development efforts.

The records used for this pseudo urban planning project are months old, and not very useful for predicting what may happen once the improvements are put in place. However, this certainly appears to be a promising start in using non-descript mobile data for improved urban interaction. If the model could eventually be adapted to make use of real-time data, life in the fast lane will get that much faster.